Guyana’s paediatric training program: a global health partnership for medical education
Bibliographic record
Abstract
Guyana is a low-middle income country on the northern coast of South America between Venezuela and Suriname. Guyana has relatively high child mortality and a notable gap in health care provision. As of 2011, there were no paediatricians in the public sector where approximately 90% of the population seek care. In response to this unmet need, Guyanese diaspora living in Canada, in partnership with Canadian paediatricians and the main teaching hospital, Georgetown Public Hospital Corporation (GPHC), developed a Master's program in paediatrics. The postgraduate program was designed with adapted training objectives from the Royal College of Physicians and Surgeons of Canada and the American Board of Paediatrics. Innovative strategies to overcome the lack of qualified paediatric faculty in Guyana included web-conferencing and a volunteer North American paediatric faculty presence at GPHC with a goal of 1-2 weeks every month. By November 2016, 10 graduates will have passed through a rigorous program of assessment including a two-day final examination with an objective structured clinical examination (OSCE) component.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".